This paper develops default priors for Bayesian analysis that reproduce familiar frequentist and Bayesian analyses for models that are exponential or location. For the vector parameter case there is an information adjustment that avoids the Bayesian marginalization paradoxes and properly targets the prior on the parameter of interest thus adjusting for any complicating nonlinearity the details ...

We present a general model for default time, making precise the role of the intensity process, and showing that this process allows for a knowledge of the conditional distribution of the default only “before the default”. This lack of information is crucial while working in a multi-default setting. In a single default case, the knowledge of the intensity process does not allow to compute the pr...

In first-order logic, a theory T1 is considered stronger than another theory T2 if every formula derived from T2 is also derived from T1. Such an order relation is useful to know relative value between different theories. In the context of default logic, a theory contains default information as well as definite information. To order default theories, it is necessary to assess the information co...